An l2-stable feedback structure for nonlinear adaptive filtering and identification

This paper proposes a feedback structure for the design of l 2-stable algorithms for nonlinear adaptive filtering and identification, and establishes explicit connections between classical schemes in IIR modeling and more recent results in H ∞ theory. In particular, two algorithms due to Feintuch an...

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Veröffentlicht in:Automatica (Oxford) Jg. 33; H. 1; S. 13 - 30
Hauptverfasser: Sayed, Ali H., Rupp, Markus
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 1997
Elsevier
Schlagworte:
Applied sciences
averaging analysis
bootstrap method
Computer science; control theory; systems
Control theory. Systems
Exact sciences and technology
feedback structure
Gauss-Newton filters
H∞ filters
IIR modeling
l2 stability
loop transformation
mean-value theorem
Modelling and identification
positive-real function
pseudo-linear regression filter
Small-gain theorem
stochastic gradient filters
averaging analysis
pseudo-linear regression filter
loop transformation
Small-gain theorem
IIR modeling
l 2 stability
positive-real function
feedback structure
stochastic gradient filters
Gauss-Newton filters
bootstrap method
H ∞ filters
mean-value theorem
Averaging method
Stability
Adaptive algorithm
Feedback system
Gauss Newton method
Adaptive filtering
Bootstrap
Real positive function
System identification
Mean value
Non linear system
ISSN:0005-1098, 1873-2836
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Beschreibung
Zusammenfassung:This paper proposes a feedback structure for the design of l 2-stable algorithms for nonlinear adaptive filtering and identification, and establishes explicit connections between classical schemes in IIR modeling and more recent results in H ∞ theory. In particular, two algorithms due to Feintuch and to Landau, as well as the so-called pseudo-linear regression and Gauss-Newton algorithms, are discussed within the framework proposed here. Additional examples and simulation results are included to illustrate the applicability of the approach to several nonlinear scenarios.
ISSN:0005-1098
1873-2836
DOI:10.1016/S0005-1098(96)00136-7